Removal of older males increases extra-pair siring success of yearling males

In animals, reproductive performance typically improves over time early in life. Several ultimate and proximate mechanisms may contribute to such an age-related improvement and these mechanisms can act in a relative or in an absolute sense. Low performance of young individuals may be the consequence of a comparison or competition with older individuals (relative), or it may be due to specific traits of young individuals and be unrelated to the presence of older competitors (absolute). Here, we perform a test to disentangle whether the effect of age class (yearling or older) on male extra-pair siring success is relative or absolute. Male age is the most consistent predictor of male extra-pair siring success across bird species, yet the mechanisms underlying this pattern are not well understood. Low extra-pair siring success of yearling males may be a consequence of the presence of older (“adult”) males (hypothesis 1), because adult males are more successful in intra- and intersexual interactions or because females prefer to copulate with adult males when available (relative preference). Alternatively, low extra-pair siring success of yearlings may be independent of the presence of adult males (hypothesis 2), for example, if yearling males on average invest less in extra-pair behavior or if females avoid them as extra-pair mates, independent of the availability of older males (absolute preference). To distinguish between these 2 hypotheses, we experimentally manipulated the age structure of a nest-box-breeding population of blue tits (Cyanistes caeruleus) by removing almost all adult males, and compared patterns of extra-pair paternity in the experimental year with those from the preceding 15 “control” years. Removal of adult males resulted in a substantial increase in the extra-pair siring success of yearling males compared to the “control” years, but did not affect the population-level frequency of extra-pair paternity or its spatial patterns. Our results provide clear evidence that extra-pair siring success of yearlings can increase and that it depends on the presence of older males in the population, indicating a relative effect of age on reproductive performance. These results suggest that older males outcompete yearling males in direct or indirect interactions, in sperm competition or as a result of differences in attractiveness to females.


SUPPORTING TABLES
-4.14 -5.29 -3.08 annual proportion of yearlings among male breeders 5.41 3.31 7.63 4.9 < 0.0001 a Binomial GLMM (estimates on logit scale, binary response variable), study year included as random intercept.N = 1681 male breeders across 16 years.b Binomial GLM (estimates on logit scale), N = 15 years.c Binomial GLMM (estimates on logit scale, binary response variable), study year and nest identity included as random intercepts.N = 940 male-female combinations as genetic parents of extra-pair offspring from 808 nests across 16 years.There is no relationship between population-wide measures of timing of breeding (mean annual lay date or mean annual hatch date) and the rate of extra-pair paternity for all years (2007)(2008)(2009)(2010)(2011)(2012)(2013)(2014)(2015)(2016)(2017)(2018)(2019)(2020)(2021)(2022) or only during the control years (all P>0.54; see [S1]).There is no relationship between lay date, hatch date, hatching success or fledging success and the occurrence of extra-pair offspring in a nest and there is no interaction with experimental year (all P>0.12; see [S1]).(2007)(2008)(2009)(2010)(2011)(2012)(2013)(2014)(2015)(2016)(2017)(2018)(2019)(2020)(2021).There is only a weak correlation between the proportion of yearling male breeders and the proportion of polygynous male breeders (Spearman rank correlation: rS = 0.15, N = 15, P = 0.59) and there is no interaction between the two effects (P = 0.38, see [S1]).(M) Proportion of breeding females that hatched from a nest on the study site in previous season.Shown are annual raw data (dots, size varies according to sample size, annual means or proportions) and model fits with their 95% confidence intervals (bars or fitted lines with shaded areas).Numbers at the bottom indicate overall sample sizes.Sample sizes differ for (K), because age information is not available for all breeding females.See Table S3 for statistical details.The models for A, B, J-M have a binary response variable (Y/N).The data and code needed to generate this Figure can be found in https://osf.io/w7fx6.

Fig A .
Fig A. Comparison of key breeding parameters between the experimental year 2022 (red) and the control years 2007 -2021 (blue).(A) Proportion of yearlings among breeding males.(B) Proportion of yearlings among breeding females.(C) Number of breeding males.(D) Number of breeding females.(E) Breeding density (number of breeding pairs, i.e. unique male-female combinations).(F) Breeding synchrony (synchrony index [S3]).(G) Laying date (day of year, 1 = 1 January).(H) Clutch size.(I) Number of hatchlings.(J) Proportion fledged (proportion of nests that produced at least one fledgling).(K) Proportion of breeding females that were breeding in previous season.(L) Proportion of breeding females first recorded in present season.(M)Proportion of breeding females that hatched from a nest on the study site in previous season.Shown are annual raw data (dots, size varies according to sample size, annual means or proportions) and model fits with their 95% confidence intervals (bars or fitted lines with shaded areas).Numbers at the bottom indicate overall sample sizes.Sample sizes differ for (K), because age information is not available for all breeding females.See TableS3for statistical details.The models for A, B, J-M have a binary response variable (Y/N).The data and code needed to generate this Figure can be found in https://osf.io/w7fx6.

Table A .
Extra-pair siring success of yearlings in 2022Extra-pair siring success of yearlings in   compared to 2007Extra-pair siring success of yearlings in   -2021

Table B .
Frequency of extra-pair paternity and its spatial pattern in 2022 compared to 2007 -2021.Binomial GLMM (estimates on logit scale, binary response variable), study year, sire identity and nest identity included as random intercepts.N = 690 malefemale combinations as genetic parents of extra-pair young from 246 nests involving 371 sires across 16 years.e Beta GLMM (estimates on logit scale), study year included as random intercept.N = 74 proportions of rank values across 16 years. d

Table C .
Comparison of breeding parameters between 2022 and the control years 2007 -2021.

Table C
The probability that a male breeder is a yearling.Binomial GLMM (estimates on logit scale, binary response variable) with year and male identity as random intercepts.N = 1685 male breeders across 16 years (2007 to 2022).bTheprobability that a female breeder is a yearling.Binomial GLMM (estimates on logit scale, binary response variable) with year and female identity as random intercepts.N = 1798 observations of 1071 females across 16 years (2007 to 2022).In 2022, yearling females had a similar probability to have extra-pair young in their nest (36 %) as adult females (30%; P = 0.58; see[S1]).This was also true in control years[S2].The probability that a female breeder was breeding in the previous season.Binomial GLMM (estimates on logit scale, binary response variable) with year and female identity as random intercepts.N = 1772 observations of 1081 females across 15 years (2008 to 2022; 2007 excluded since no data from previous year available).l The probability that a female breeder was first recorded in the focal season.Binomial GLMM (estimates on logit scale, binary response variable) with year and female identity as random intercepts.N = 1772 observations of 1081 females across 15 years (2008 to 2022; 2007 excluded since no data from previous year available).m The probability that a female breeder hatched from a nest on the study site in the previous season.Binomial GLMM (estimates on logit scale, binary response variable) with year and female identity as random intercepts.N = 1772 observations of 1081 females across 15 years (2008 to 2022; 2007 excluded since no data from previous year available).
a c Linear model (LM); N = 16 years (2007 to 2022).d LM; N = 16 years (2007 to 2022).e Number of breeding pairs, i.e. unique male-female combinations; LM; N = 16 years (2007 to 2022).f Breeding synchrony index [S3]; LM; N = 16 years (2007 to 2022).g Laying date measured as day of year (1 = 01.January).Linear mixed model (LMM) with year and female identity as random intercepts.N = 1898 clutches of 1120 females across 16 years (2007 to 2022).h LMM with female age class (yearling or adult) and laying date (centralized within year) as covariates and study year and female identity as random intercepts.A model without the covariates gave similar results (not shown).N = 1847 clutches of 1071 females across 16 years (2007 to 2022).i Poisson GLMM (estimates on log scale) with clutch size as covariate and year and female identity as random intercepts.N = 1877 broods of 1110 females across 16 years (2007 to 2022).j Probability that a given nest produced at least one fledgling.Binomial GLMM (estimates on logit scale, binary response variable) with year and female identity as random intercepts.N = 1900 broods of 1122 females across 16 years (2007 to 2022).k

Table D .
Comparison of the frequency of social polygyny between 2022 and the control years 2007 -2021.The probability that a male breeder is socially polygynous.Binomial GLMM (estimates on logit scale, binary response variable) with year as random intercept.N = 1735 breeding males across 16 years (2007 to 2022).

Table E .
The effects of the proportion of yearlings among male breeders and the proportion of socially polygynous males on extra-pair siring success of yearlings in the control years 2007-2021.

Table F .
Basic parentage metadata and contextual information for the present study a .Difference to total number of offspring sampled explained by young with unassigned paternity status.
a Information following[S4].bTableshows data from all monitored nests.Models may include smaller sample, if some information is unavailable.See[S1]for data selection for each model.c